• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.213-220
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• 2015

This study was carried out to determine the distribution of factors as effected by debris flow in Ulsan and Pusan metropolitan areas because mainly debris flow caused by typhoons and local heavy rainfall events is mainly attributed to damage of human being ad property. The high risk degree of debris flow was to affected by east (20%), northeast (20%) and northwest (20%) slopes with stand age class with elevation (69%) of 100-200 (33%). Also, the risk was high in high erosion collapse degree with slope degree of $20-25^{\circ}$ with over 300 mm (100%) of maximum daily rainfall events and 50-100 mm (50%) or >100 mm (50%) of maximum hourly rainfall events with <5 km of stream path and <50 ha of catchment area. Landslide debris and wood residue flow was also related to igneous rocks (73%) and bank collapse types of debrs flow (57%).

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.585-596
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• 2022

This study determined major factors influencing landslide occurrence along a forest road near Sangsan village, Sancheok-myeon, Chungju-si, Chungcheongbuk-do, South Korea. Within a 2 km radius of the study area, landslides occur intensively during periods of heavy rainfall (August 2020). This makes study of the area advantageous, as it allows examination of the influence of only geological and tomographic factors while excluding the effects of rainfall and vegetation. Data for 82 locations (37 experiencing landslides and 45 not) were obtained from geological surveys, laboratory tests, and geo-spatial analysis. After some data preprocessing (e.g., error filtering, minimum-maximum normalization, and multicollinearity), structural equation model (SEM) and logistic regression (LR) analyses were conducted. These showed the regolith thickness, porosity, and saturated unit weight to be the factors most influential of landslide risk in the study area. The sums of the influence magnitudes of these factors are 71% in SEM and 83% in LR.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1847-1859
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• 2022

Wildfires in North Korea can have a directly or indirectly affect South Korea if they go south to the Demilitarized Zone. Therefore, this study calculates the regional optimized Forest Fire Danger Index (FFDI) based on Local Data Assessment and Prediction System (LDAPS) weather data to obtain forest fire risk in North Korea, and applied it to the cases in Goseong-gun and Cheorwon-gun, North Korea in April 2022. As a result, the suitability was confirmed as the FFDI at the time of ignition corresponded to the risk class Extreme and Severe sections, respectively. In addition, a qualitative comparison of the risk map and the soil moisture map before and after the wildfire, the correlation was grasped. A new forest fire risk index that combines drought factors such as soil moisture, Standardized Precipitation Index (SPI), and Normalized Difference Water Index (NDWI) will be needed in the future.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.938-945
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• 2008

Rockfall and landslide bring about a great social loss with loss of property such as obstruction of traffic and damage of the crops as well as casualty. The purpose of this study is to develop a mobile equipment for local risk factors detecting of road slope. The mobile equipment is designed to receive the sensing data from the measurement sensors, which are installed to detect the dangerous signs from the slopes, as loaded on a vehicle which is moving around to the places where the sensors are installed. In general, more than one mandatory data logger, which is very expensive, must be installed at each slope for the automatic measuring system, but in case of this developmental system, the inexpensive routine measurement can be performed regardless of the number of slopes due to the single unit of information gathering vehicle. This study is going to develop technologies that are expected to be applied to not only slope but also tunnel and bridges which might have the partial risk and need measuring.

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.

• The Journal of Engineering Geology
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• v.27 no.3
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• pp.275-292
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• 2017

Large landslides have occurred more than 100 times each year by the influence of cyclones and torrential rain each year in Vietnam. Nevertheless, they $don^{\circ}$Øt have a scientific management system to prevent the landslide disaster in advance. In this study, we acquisited the risk factor and damage factor about each slope throughout basic survey at Hoang Sa Costal Road, Danang and at Ho Chi Minh Road, Quangnam. The priority of investigation technique considering of the exposure characteristics of the road slope in Vietnam developed by the empirical analysis with the acquired data. As a result, we can set the foundation of scientific management for the road slope management system in Vietnam.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.191-198
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• 2010

The displacement of slope is a key factor in predicting the risk of a landslide. Therefore, the slope displacement should be continuously observed with high accuracy. Recently, high-tech equipment such as optical fiber sensor, GPS, total station and measuring instrument have been used. However, such equipment is poorly used in fields due to economics, environment, convenience and management. Because of this, development of substantial observational techniques for varied slope observation and field applications is needed. This study analyzed the possibility of terrestrial LiDAR for slope monitoring and suggested it as information-obtaining technique for slope investigation and management. For that, this study evaluated the monitoring accuracy of terrestrial LiDAR and performed GRID analysis to read the displacement area with the naked eye. In addition, it suggested a methodology for slope monitoring.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.9-9
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• 2000

For the planning of future land use for economic activities, an essential component is the identification of the vulnerable areas for natural hazard and environmental impacts from the activities. Also, exploration for mineral and energy resources is carried out by a step by step approach. At each step, a selection of the target area for the next exploration strategy is made based on all the data harnessed from the previous steps. The uncertainty of the selected target area containing undiscovered resources is a critical factor for estimating the exploration risk. We have developed not only spatial prediction models based on adapted artificial intelligence techniques to predict target and vulnerable areas but also validation techniques to estimate the uncertainties associated with the predictions. The prediction models will assist the scientists and decision-makers to make two critical decisions: (i) of the selections of the target or vulnerable areas, and (ii) of estimating the risks associated with the selections.

• Journal of the Korean GEO-environmental Society
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• v.18 no.6
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• pp.21-30
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• 2017

This study analyzed the ground characteristics region by designating 3 research areas, Anrim-dong in Chungju City, Busa-dong in Daejeon Metropolitan City and Sinan-dong in Andong City out of the areas subject to concentrated management to prepare for sediment disaster in downtown areas. The correlation between ground characteristics were observed by using characteristics (crown density, root cohesion, rainfall characteristics, soil characteristics) and the risk areas were predicted through sediment disaster prediction modeling. Landslide MAPping (LSMAP), Stability Index MAPping (SINMAP) and Landslide Hazard MAP (LHMAP) were used for the comparative analysis of the hazard prediction model for sediment disaster. As a result of predicting the sediment disaster danger, in case of SINMAP which was generally used, excessive range was predicted as a hazardous area and in case of the Korea Forest Service's landslide hazard map (LHMAP), the smallest prediction area was assessed. LSMAP predicted a medium range of SINMAP and LHMAP as hazardous area. The difference of the prediction results is that the analysis parameters of LSMAP is more diverse and engineering than two comparative models, and it is found that more precise prediction is possible.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.675-686
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• 2021

This study investigated the ground displacement occurring in a slope below a waste-rock dumping site and estimated the likelihood of a disaster due to a landslide. To start with, photogrammetry was conducted by unmanned aerial vehicles (UAVs) to investigate the size and extent of the ground displacement. From April 2019 to July 2020, the average error rate of the five UAV surveys was 0.011-0.034 m, and an elevation change of 2.97 m occurred due to the movement of the soil layer. Only some areas of the slope showedelevation change, and this was believed to be due to thegroundwater generated during rainfall rather than the effect of the waste-rock load at the top. Sensitivity analysis for LS-RAPID simulation was performed, and the simulation results were compared and analyzed by applying a digital elevation model (DEM) and a digital surface model (DSM)as terrain data with 10 m, 5 m, and 4 m grids. When data with high spatial resolution were used, the extent of the sedimentation of landslide material tended to be excessively expanded in the DEM. In contrast, in the result of applying a DSM, which reflects the topography in detail, the diffusion range was not significantly affected even when the spatial resolution was changed, and the sedimentation behavior according to the river shape could be accurately expressed. As a result, it was concluded that applying a DSM rather than a DEM does not significantly expand the sedimentation range, and results that reflect the site situation well can be obtained.